Diagnosis of Polycystic Ovary Syndrome Using Deep Learning Neural Network-based Segmentation Techniques

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder affecting women of reproductive age, characterized by hormonal imbalances leading to symptoms like irregular menstrual cycles, excessive hair growth, and infertility. Early detection is crucial for effective management and prevention of associated health risks. Our study proposes a comprehensive approach to PCOS detection, utilizing preprocessing and segmentation techniques followed by Convolutional Neural Network (CNN) based classification. The preprocessing phase enhances image quality and features through flipping, rotation, zooming, and rescaling, standardizing inputs for analysis. Segmentation techniques isolate regions of interest, employing size-based filtering, contour detection, density analysis, color-based segmentation, edge detection, texture analysis, location-based segmentation, and watershed transformation to delineate structures effectively. Central to our methodology is CNN-based feature extraction and classification, achieving high accuracy in distinguishing PCOS-positive and PCOS-negative cases. Integrating diverse techniques enables effective preprocessing and segmentation, while CNNs automate feature extraction, enhancing model generalization. Our approach presents a promising strategy for accurate PCOS detection from medical images, aiding clinicians in early diagnosis and management, thereby improving patient outcomes and quality of care.